The existing freespace detection methods are mostly designed for on-road navigation and most of them fuse the LiDAR and camera information together for accurate freespace detection. RBNet [1]} detects both road and road boundary in a single process and models them with a Bayesian network. TVFNet [2]} takes the LiDAR imageries and the camera-perspective maps as inputs and outputs pixel-wise road detection results in both the LiDAR's imagery view and the camera's perspective view simultaneously. LC-CRF [3]} detects road with LiDAR-camera fusion in a conditional random field (CRF) framework to exploit both range and color information. RBANet [4]} proposes the reverse attention and boundary attention units for road segmentation. PLARD [5]} introduces a progressive LiDAR adaptation-aided road detection approach to adapt LiDAR information into visual image-based road detection and improve detection performance. SNE-RoadSeg [6]} infers surface normal information from dense depth images and fuses it with image information to improve the freespace detection performance. The above methods are focused on the on-road freespace detection. In this paper, we introduce a novel Transformer architecture based method for off-road freespace detection and do extensive experiments on our off-road dataset.
